Department of Medicine, University of Toronto, Toronto, ON, Canada.
Evaluative Clinical Sciences Program, Sunnybrook Research Institute, Toronto, ON, Canada.
Med Decis Making. 2023 Feb;43(2):183-190. doi: 10.1177/0272989X221121343. Epub 2022 Sep 5.
Diagnostic reasoning requires clinicians to think through complex uncertainties. We tested the possibility of a bias toward an available single diagnosis in uncertain cases.
We developed 5 different surveys providing a succinct description of a hypothetical individual patient scenaric. Each scenario was formulated in 2 versions randomized to participants, with the versions differing only in whether an alternative diagnosis was present or absent. The 5 scenarios were designed as separate tests of robustness using diverse cases, including a cautious scenario, a risky scenario, a sophisticated scenario, a validation scenario, and a comparative scenario (each survey containing only 1 version of 1 scenario). Participants included community members ( = 1104) and health care professionals ( = 200) who judged the chances of COVID infection in an individual patient.
The first scenario described a cautious patient and found a 47% reduction in the estimated odds of COVID when a flu diagnosis was present compared with absent (odds ratio = 0.53, 95% confidence interval 0.30 to 0.94, = 0.003). The second scenario described a less cautious patient and found a 70% reduction in the estimated odds of COVID in the presence of a flu diagnosis (odds ratio = 0.30, 95% confidence interval 0.13 to 0.70, < 0.001). The third was a more sophisticated scenario presented to medical professionals and found a 73% reduction in the estimated odds of COVID in the presence of a mononucleosis diagnosis (odds ratio = 0.27, 95% confidence interval 0.10 to 0.75, < 0.001). Two further scenarios-avoiding mention of population norms-replicated the results.
Brief hypothetical scenarios may overestimate the extent of bias in more complicated medical situations.
These results demonstrate that an available simple diagnosis can lead individuals toward premature closure and a failure to fully consider additional severe diseases.
Occum's razor has been debated for centuries yet rarely subjected to experimental testing for evidence-based medicine.This article offers direct evidence that people favor an available simple diagnosis, thereby neglecting to consider additional serious diseases.The bias can lead individuals to mistakenly lower their judged likelihood of COVID or another disease when an alternate diagnosis is present.This misconception over the laws of probability appears in judgments by community members and by health care workers.The pitfall in reasoning extends to high-risk cases and is not easily attributed to information, incentives, or random chance.
诊断推理要求临床医生全面考虑复杂的不确定性。我们测试了在不确定情况下对单一可诊断结果的偏向可能性。
我们设计了 5 个不同的调查，简洁描述了一个假设的个体患者场景。每个场景都以两种版本呈现给参与者，这两种版本仅在是否存在其他诊断方面存在差异。这 5 个场景分别使用不同的案例进行了稳健性测试，包括一个谨慎的场景、一个冒险的场景、一个复杂的场景、一个验证场景和一个比较场景（每个调查仅包含 1 个场景的 1 个版本）。参与者包括社区成员（=1104）和医疗保健专业人员（=200），他们判断个体患者感染 COVID 的可能性。
第一个场景描述了一个谨慎的患者，当存在流感诊断时，COVID 的估计几率降低了 47%（比值比=0.53，95%置信区间 0.30 至 0.94，=0.003）。第二个场景描述了一个不太谨慎的患者，当存在流感诊断时，COVID 的估计几率降低了 70%（比值比=0.30，95%置信区间 0.13 至 0.70，<0.001）。第三个场景是一个更复杂的场景，提供给医疗专业人员，当存在单核细胞增多症诊断时，COVID 的估计几率降低了 73%（比值比=0.27，95%置信区间 0.10 至 0.75，<0.001）。另外两个避免提及人群标准的场景复制了这些结果。
简短的假设场景可能会高估更复杂医疗情况下的偏见程度。
这些结果表明，一个可用的简单诊断可以导致个体过早地做出结论，并且无法充分考虑其他严重疾病。
奥卡姆剃刀已经争论了几个世纪，但很少有实证医学对其进行实验测试。本文提供了直接证据，证明人们倾向于一个可用的简单诊断，从而忽略了考虑其他严重疾病。这种偏见会导致个体在存在替代诊断时错误地降低他们判断 COVID 或其他疾病的可能性。这种对概率论法则的误解出现在社区成员和医疗保健工作者的判断中。这种推理上的陷阱延伸到高风险情况，并且不容易归因于信息、激励或随机机会。
